{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.metrics import mean_squared_error,r2_score\n",
    "from sklearn import linear_model,datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.03807591,  0.05068012,  0.06169621, ..., -0.00259226,\n",
       "         0.01990842, -0.01764613],\n",
       "       [-0.00188202, -0.04464164, -0.05147406, ..., -0.03949338,\n",
       "        -0.06832974, -0.09220405],\n",
       "       [ 0.08529891,  0.05068012,  0.04445121, ..., -0.00259226,\n",
       "         0.00286377, -0.02593034],\n",
       "       ...,\n",
       "       [ 0.04170844,  0.05068012, -0.01590626, ..., -0.01107952,\n",
       "        -0.04687948,  0.01549073],\n",
       "       [-0.04547248, -0.04464164,  0.03906215, ...,  0.02655962,\n",
       "         0.04452837, -0.02593034],\n",
       "       [-0.04547248, -0.04464164, -0.0730303 , ..., -0.03949338,\n",
       "        -0.00421986,  0.00306441]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the diabetes dataset\n",
    "diabetes = datasets.load_diabetes()\n",
    "diabetes.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "       [ 0.05522933],\n",
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       "       [ 0.03906215],\n",
       "       [-0.0730303 ]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use only one feature\n",
    "diabetes_X = diabetes.data[:, 2]\n",
    "diabetes_X[:,np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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